The Innovator's Solution

How manufacturers optimize their inventories

Editor’s Note: Microsoft’s Colin Masson, Global Industry Director of Manufacturing Solutions (that's him on the left), sat down with me to discuss how manufacturers optimize their inventories. You can see a longer version of the interview hereon the Microsoft website or my slightly abridged version below.

Colin: What are some of the global trends currently impacting demand modeling, supply chain planning, and supply variability?

Jeff: We all know that globalization is affecting the supply chain and companies don’t have the same flexibility with local resources as they did a decade or two ago. But that’s not the only shift in manufacturing. For example, there has been a dramatic increase in demand volatility and consumer demand is shifting faster than ever. Data availability and advanced analytics are also shaping the industry by providing new insights into efficient manufacturing processes. Finally, product proliferation is driving supply variability, as manufacturers struggle to manage a variety of product.

Jeff: Accomplishing real inventory optimization (IO) provides manufacturers a leg up from their ineffective legacy processes such as rules of thumb or guesswork. IO uses powerful analytics to generate the right inventories to meet demand, address demand volatility, and achieve targeted service levels.

Multi-echelon inventory optimization (MEIO) goes even further, looking at optimization from a global perspective, across all echelons of the supply chain. Optimizing for one stage of the supply chain works great for that specific echelon, but doesn’t necessarily address the needs of the others. It’s very common for manufacturers to optimize well at one level, such as reducing costs through long runs, but with incorrect inventory to meet demand in downstream locations. You might also see the opposite problem, customer-centric organizations that don’t consider the needs of the manufacturing or distribution network.

MEIO allows you to solve the problem across the entire supply chain by creating a probability-based model of demand and inventory. It then uses this model to understand the impact of demand forecasting on inventory. When done correctly, manufacturers can set achievable and clear service level goals for meeting customer needs, balancing business constraints and financial targets for the first time. Additionally, manufacturers can achieve these goals with a high level of certainty, consistency, and sustainability. It’s the ability to hit goals every single month and to progressively get better as time goes on that makes MEIO such a powerful solution and a factor of pride and accomplishment for the customers that I talk to.

Colin: What kind of results have customers seen with ToolsGroup MEIO?

Jeff: Our customers have consistently seen higher service levels with less inventory on hand. As an example, a company that normally hits 96% service level was able to increase to 98% while simultaneously cutting inventory 20-25%. Another consistent result that customers see is reduced operational expense. This can be anything from reduced logistics cost, fewer manufacturing changeovers, less expediting, and reduced low-quality inventory like perishable goods.

We are also seeing companies appreciate the ability to reduce the cost of manual forecasting and inventory adjustment processes that tie up good employees in mundane tasks. In addition to improving service levels and cutting costs, intelligent, highly automated MEIO empowers organizations to make the most of their employees’ industry intelligence.

Colin: What is the implementation process like?

Jeff: There are three major implementation areas: analytics, data, and change management. Most people think that analytics is the hardest part of implementing inventory optimization, but that couldn’t be farther from the truth. Modern analytics processes are highly automated and the software has become capable of solving problems in an autonomous manner. Therefore, if the business gives the tool the data it needs, it can do the rest with minimal maintenance.

Obviously, you need data to drive this. Most of the required information is already encapsulated in common ERP and order management systems, but often hasn’t been properly maintained. So, the biggest piece of an implementation can be cleaning up the source data.

Finally, change management is needed. Many users are accustomed to constantly attending to the process, but once you have a good system in place that extra attention can actually dampen the benefits. Users have to learn to take a step back and focus on the big picture.

Colin: What opportunities does a cloud-based platform provide?

Jeff: The cloud offers an excellent platform for SaaS implementation and provides an easier, lower cost accessibility than creating IT infrastructure from scratch. It also helps facilitate the interaction between ToolsGroup and our customers, enabling us to easily collaborate. Finally, a platform like Azure is instrumental to aggregating, analyzing, and making data available to continuously improve forecasts and inventory optimization.

Colin: Let’s expand into that new opportunity. What differences do customers see when they start to leverage both data they already have in their ERP systems, but also cloud-enabled data like IoT from smart products, weather forecasts, or economic trend data? Can you give examples of how that improves the MEIO process?

Jeff: First, I’d like to highlight that the benefits of performing inventory optimization with data already available in ERP or sales order management systems creates significant improvements in key performance indicators, such as customer service levels, operational costs, and working capital. Once companies achieve these results, the question becomes “how do we go to the next step and improve even more?” That’s where external data sources come in.

For example, an HVAC manufacturer may already have a sense that their products have a certain seasonality and that seasonality might be built into forecasts based on internal data. But they also know that sales could be impacted by extreme weather conditions. Incorporating weather data directly into their model gives that manufacturer a leg up. Even reducing latency by one or two weeks can have a big impact on the ability to meet incremental demand. Additionally, some of our customers are looking at standard web data and social listening practices. For instance, an electronics company can track how often a new spec sheet is downloaded and use that as an early predictor for demand.

Colin: Those were some great examples. Clearly ToolsGroup is recognized as a leader in the supply chain space. What do you attribute that success to? What makes ToolsGroup’s MEIO so much better than competing solutions?

Jeff: We understood the need for forecasting and managing long tail demand very early on, so we had a huge head start to develop the best solution to address the problem. ToolsGroup has also been ahead of the curve when it comes to machine learning. We had our first entry into machine learning seven years ago, and Gartner featured a case study on our successful implementation at Dannon, the well-known dairy company. We have years of experience fine-tuning the best multi-echelon inventory optimization solution with proprietary algorithms and powerful machine learning.

We are also extremely dedicated to helping our customers achieve success. Many solution providers simply focus on how customers can improve the way they currently operate. At ToolsGroup, we are always challenging how customers can better accomplish their goals and operate more efficiently and effectively today and tomorrow. Our customers are clearly impressed with our approach, and it’s something we are incredibly proud of.

Jeff: Microsoft’s industry-leading platforms like Azure and Cortana are the perfect collaborative solutions for what ToolsGroup provides. The learning algorithms found in Cortana Intelligence and Microsoft’s incredible market presence and ease of implementation with Azure set us up for success. We’re a specialized company, focused on being the best in the world at solving a specific problem. Microsoft enables us to reach a broad range of companies, making this partnership a great fit.

Colin: Finally, what does the future of supply chain planning look like?

Jeff: This is a surprisingly easy question to answer. In the words of William Gibson, “the future is already here—it’s just not very evenly distributed.” As manufacturers look to achieve digital transformation, we are seeing a dramatic shift away from outdated inventory planning solutions like rules of thumb and gut instinct. Some manufacturers have already begun to embrace the future, leveraging machine learning and new data streams like weather patterns or IoT-connected products to achieve their customer service level targets, and we expect the industry to continue with this trend. We’ve just barely scratched the surface.

Additionally, we’ve seen an increased focus on the supply chain planner role. It’s no secret that planners often perform menial tasks to complete their core job. Automated, probability-based solutions are dramatically increasing employee efficiency so planners can focus on the things they really do best: working with people, understanding the market, and focusing on suppliers and customers. Planners can then feed market intelligence into the system and let it perform the computational work instead of being dragged into the weeds of solving the problem in the first place.

Click below if you are considering next steps in inventory optimization: